Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations

TL;DR

Surface Edge Explorer (SEE) is a novel NBV planning method that uses surface boundary density to efficiently explore 3D scenes, outperforming volumetric methods in coverage and computation time.

Contribution

SEE introduces a scene-model-free NBV approach leveraging density-based surface boundary detection, avoiding complex parameter tuning and multiple survey stages.

Findings

Better surface coverage than state-of-the-art volumetric methods

Lower computation time for equivalent scene coverage

Efficient exploration of 3D scenes without scene modeling

Abstract

Surveying 3D scenes is a common task in robotics. Systems can do so autonomously by iteratively obtaining measurements. This process of planning observations to improve the model of a scene is called Next Best View (NBV) planning. NBV planning approaches often use either volumetric (e.g., voxel grids) or surface (e.g., triangulated meshes) representations. Volumetric approaches generalise well between scenes as they do not depend on surface geometry but do not scale to high-resolution models of large scenes. Surface representations can obtain high-resolution models at any scale but often require tuning of unintuitive parameters or multiple survey stages. This paper presents a scene-model-free NBV planning approach with a density representation. The Surface Edge Explorer (SEE) uses the density of current measurements to detect and explore observed surface boundaries. This approach is shown experimentally to provide better surface coverage in lower computation time than the evaluated state-of-the-art volumetric approaches while moving equivalent distances.